Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.

BACKGROUND: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-level factors, and SUD treatment engagement. METHODS: This was a secondary analysis of the Global Appraisal of Individual Needs (GAIN) dataset and United States Census Bureau data utilizing random forest machine learning and generalized linear mixed modelling. Our sample (N = 15,873) included all people entering SUD treatment at GAIN sites from 2006 to 2012. Predictors included an array of demographic, psychosocial, treatment-specific, and clinical measures, as well as environment-level measures for the neighborhood in which patients received treatment. RESULTS: Greater odds of treatment engagement were predicted by adolescent age and psychiatric comorbidity, and at the neighborhood-level, by low unemployment and high population density. Lower odds of treatment engagement were predicted by Black/African American race, and at the neighborhood-level by high rate of public assistance and high income inequality. Regardless of the degree of treatment engagement, individuals receiving treatment in areas with high unemployment, alcohol sale outlet concentration, and poverty had greater substance use and related problems at baseline. Although these differences reduced with treatment and over time, disparities remained. CONCLUSIONS: Neighborhood-level factors appear to play an important role in SUD treatment engagement. Regardless of whether individuals engage with treatment, greater loading on social determinants of health such as unemployment, alcohol sale outlet density, and poverty in the therapeutic landscape are associated with worse SUD treatment outcomes.

A Statistical Analysis of the Impact of Gun Ownership on Mass Shootings in the USA Between 2013 and 2022.

Mass shootings (incidents with four or more people shot in a single event, not including the shooter) are becoming more frequent in the United States, posing a significant threat to public health and safety in the country. In the current study, we intended to analyze the impact of state-level prevalence of gun ownership on mass shootings-both the frequency and severity of these events. We applied the negative binomial generalized linear mixed model to investigate the association between gun ownership rate, as measured by a proxy (i.e., the proportion of suicides committed with firearms to total suicides), and population-adjusted rates of mass shooting incidents and fatalities at the state level from 2013 to 2022. Gun ownership was found to be significantly associated with the rate of mass shooting fatalities. Specifically, our model indicated that for every 1-SD increase-that is, for every 12.5% increase-in gun ownership, the rate of mass shooting fatalities increased by 34% (p value < 0.001). However, no significant association was found between gun ownership and rate of mass shooting incidents. These findings suggest that restricting gun ownership (and therefore reducing availability to guns) may not decrease the number of mass shooting events, but it may save lives when these events occur.

Ecological and anthropogenic drivers of waterfowl productivity are synchronous across species, space, and time.

Knowledge of interspecific and spatiotemporal variation in demography-environment relationships is key for understanding the population dynamics of sympatric species and developing multispecies conservation strategies. We used hierarchical random-effects models to examine interspecific and spatial variation in annual productivity in six migratory ducks (i.e., American wigeon [Mareca americana], blue-winged teal [Spatula discors], gadwall [Mareca strepera], green-winged teal [Anas crecca], mallard [Anas platyrhynchos] and northern pintail [Anas acuta]) across six distinct ecostrata in the Prairie Pothole Region of North America. We tested whether breeding habitat conditions (seasonal pond counts, agricultural intensification, and grassland acreage) or cross-seasonal effects (indexed by flooded rice acreage in primary wintering areas) better explained variation in the proportion of juveniles captured during late summer banding. The proportion of juveniles (i.e., productivity) was highly variable within species and ecostrata throughout 1961-2019 and generally declined through time in blue-winged teal, gadwall, mallard, pintail, and wigeon, but there was no support for a trend in green-winged teal. Productivity in Canadian ecostrata declined with increasing agricultural intensification and increased with increasing pond counts. We also found a strong cross-seasonal effect, whereby more flooded rice hectares during winter resulted in higher subsequent productivity. Our results suggest highly consistent environmental and anthropogenic effects on waterfowl productivity across species and space. Our study advances our understanding of current year and cross-seasonal effects on duck productivity across a suite of species and at finer spatial scales, which could help managers better target working-lands conservation programs on both breeding and wintering areas. We encourage other researchers to evaluate environmental drivers of population dynamics among species in a single modeling framework for a deeper understanding of whether conservation plans should be generalized or customized given limited financial resources.

RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION.

Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.

Association of the medication protocols and longitudinal change of COVID-19 symptoms: a hospital-based mixed-statistical methods study.

The objective of this study was to identify the relationship between hospitalization treatment strategies leading to change in symptoms during 12-week follow-up among hospitalized patients during the COVID-19 outbreak. In this article, data from a prospective cohort study on COVID-19 patients admitted to Khorshid Hospital, Isfahan, Iran, from February 2020 to February 2021, were analyzed and reported. Patient characteristics, including socio-demographics, comorbidities, signs and symptoms, and treatments during hospitalization, were investigated. Also, to investigate the treatment effects adjusted by other confounding factors that lead to symptom change during follow-up, the binary classification trees, generalized linear mixed model, machine learning, and joint generalized estimating equation methods were applied. This research scrutinized the effects of various medications on COVID-19 patients in a prospective hospital-based cohort study, and found that heparin, methylprednisolone, ceftriaxone, and hydroxychloroquine were the most frequently prescribed medications. The results indicate that of patients under 65 years of age, 76% had a cough at the time of admission, while of patients with Cr levels of 1.1 or more, 80% had not lost weight at the time of admission. The results of fitted models showed that, during the follow-up, women are more likely to have shortness of breath (OR = 1.25; P-value: 0.039), fatigue (OR = 1.31; P-value: 0.013) and cough (OR = 1.29; P-value: 0.019) compared to men. Additionally, patients with symptoms of chest pain, fatigue and decreased appetite during admission are at a higher risk of experiencing fatigue during follow-up. Each day increase in the duration of ceftriaxone multiplies the odds of shortness of breath by 1.15 (P-value: 0.012). With each passing week, the odds of losing weight increase by 1.41 (P-value: 0.038), while the odds of shortness of breath and cough decrease by 0.84 (P-value: 0.005) and 0.56 (P-value: 0.000), respectively. In addition, each day increase in the duration of meropenem or methylprednisolone decreased the odds of weight loss at follow-up by 0.88 (P-value: 0.026) and 0.91 (P-value: 0.023), respectively (among those who took these medications). Identified prognostic factors can help clinicians and policymakers adapt management strategies for patients in any pandemic like COVID-19, which ultimately leads to better hospital decision-making and improved patient quality of life outcomes.

Anticoagulant Rodenticide Toxicity in Terrestrial Raptors: Tools to Estimate the Impact on Populations in North America and Globally.

Anticoagulant rodenticides (ARs) have caused widespread contamination and poisoning of predators and scavengers. The diagnosis of toxicity proceeds from evidence of hemorrhage, and subsequent detection of residues in liver. Many factors confound the assessment of AR poisoning, particularly exposure dose, timing and frequency of exposure, and individual and taxon-specific variables. There is a need, therefore, for better AR toxicity criteria. To respond, we compiled a database of second-generation anticoagulant rodenticide (SGAR) residues in liver and postmortem evaluations of 951 terrestrial raptor carcasses from Canada and the United States, 1989 to 2021. We developed mixed-effects logistic regression models to produce specific probability curves of the toxicity of ∑SGARs at the taxonomic level of the family, and separately for three SGARs registered in North America, brodifacoum, bromadiolone, and difethialone. The ∑SGAR threshold concentrations for diagnosis of coagulopathy at 0.20 probability of risk were highest for strigid owls (15 ng g-1) lower and relatively similar for accipitrid hawks and eagles (8.2 ng g-1) and falcons (7.9 ng g-1), and much lower for tytonid barn owls (0.32 ng g-1). These values are lower than those we found previously, due to compilation and use of a larger database with a mix of species and source locations, and also to refinements in the statistical methods. Our presentation of results on the family taxonomic level should aid in the global applicability of the numbers. We also collated a subset of 440 single-compound exposure events and determined the probability of SGAR-poisoning symptoms as a function of SGAR concentration, which we then used to estimate relative SGAR toxicity and toxic equivalence factors: difethialone, 1, brodifacoum, 0.8, and bromadiolone, 0.5. Environ Toxicol Chem 2024;43:988-998. © 2024 His Majesty the King in Right of Canada and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC Reproduced with the permission of the Minister of Environment and Climate Change Canada.

New horizons for comparative studies and meta-analyses.

Comparative analyses and meta-analyses are key tools to elucidate broad biological principles, yet the two approaches often appear different in purpose. We propose an integrated approach that can generate deeper insights into ecoevolutionary processes. Marrying comparative and meta-analytic approaches will allow for (i) a more accurate investigation of drivers of biological variation, (ii) a greater ability to account for sources of non-independence in experimental data, (iii) more effective control of publication bias, and (iv) improved transparency and reproducibility. Stronger integration of meta-analytic and comparative studies can also broaden the scope from species-centric investigations to community-level responses and function-valued traits (e.g., reaction norms). We illuminate commonalities, differences, and the transformative potential of combining these methodologies for advancing ecology and evolutionary biology.

Fixed and random effect selections in generalized linear mixed models.

Generalized linear mixed models are commonly used to describe relationships between correlated responses and covariates in medical research. In this paper, we propose a simple and easily implementable regularized estimation approach to select both fixed and random effects in generalized linear mixed model. Specifically, we propose to construct and optimize the objective functions using the confidence distributions of model parameters, as opposed to using the observed data likelihood functions, to perform effect selections. Two estimation methods are developed. The first one is to use the joint confidence distribution of model parameters to perform simultaneous fixed and random effect selections. The second method is to use the marginal confidence distributions of model parameters to perform the selections of fixed and random effects separately. With a proper choice of regularization parameters in the adaptive LASSO framework, we show the consistency and oracle properties of the proposed regularized estimators. Simulation studies have been conducted to assess the performance of the proposed estimators and demonstrate computational efficiency. Our method has also been applied to two longitudinal cancer studies to identify demographic and clinical factors associated with patient health outcomes after cancer therapies.

Child stunting prevalence determination at sector level in Rwanda using small area estimation.

BACKGROUND: Stunting among children under 5 years of age remains a worldwide concern, with 148.1 million (22.3%) stunted in 2022. The recent 2019/2020 Rwanda Demographic Health Survey (RDHS) revealed that the prevalence of stunting in Rwanda among under five children was 33.5%. In Rwanda, there is no sufficient evidence on stunting status to guide prioritized interventions at the sector level, the lowest administrative unit for implementing development initiatives. This study aimed to provide reliable estimates of stunting prevalence in Rwanda at the sector level. METHODS: In this article, Small Area Estimation (SAE) techniques were used to provide sector level estimates of stunting prevalence in children under five in Rwanda. By plugging in relevant significant covariates in the generalized linear mixed model, model-based estimates are produced for all sectors with their corresponding Mean Square Error (MSE). RESULTS: The findings showed that, overall, 40 out of 416 sectors had met the national target of having a stunting rate less than or equal to 19%, while 194 sectors were far from meeting this target, having a stunting rate higher than the national prevalence of 33.5% in the year 2020. The majority of the sectors with stunting prevalence that were higher than the national average of 33.5% were found in the Northern Province with 68 sectors out of 89 and in Western Province with 64 sectors out of 96. In contrast, the prevalence of stunting was lower in the City of Kigali where 14 out of 35 sectors had a stunting rate between 0 and 19%, and all sectors were below the national average. This study showed a substantial connection between stunting and factors such as household size, place of residence, the gender of the household head, and access to improved toilet facilities and clean water. CONCLUSION: The results of this study may guide and support informed policy decisions and promote localised and targeted interventions in Rwanda's most severely affected sectors with a high stunting prevalence in Rwanda.

Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes.

Cluster randomized trials (CRTs) refer to a popular class of experiments in which randomization is carried out at the group level. While methods have been developed for planning CRTs to study the average treatment effect, and more recently, to study the heterogeneous treatment effect, the development for the latter objective has currently been limited to a continuous outcome. Despite the prevalence of binary outcomes in CRTs, determining the necessary sample size and statistical power for detecting differential treatment effects in CRTs with a binary outcome remain unclear. To address this methodological gap, we develop sample size procedures for testing treatment effect heterogeneity in two-level CRTs under a generalized linear mixed model. Closed-form sample size expressions are derived for a binary effect modifier, and in addition, a computationally efficient Monte Carlo approach is developed for a continuous effect modifier. Extensions to multiple effect modifiers are also discussed. We conduct simulations to examine the accuracy of the proposed sample size methods. We present several numerical illustrations to elucidate features of the proposed formulas and to compare our method to the approximate sample size calculation under a linear mixed model. Finally, we use data from the Strategies and Opportunities to Stop Colon Cancer in Priority Populations (STOP CRC) CRT to illustrate the proposed sample size procedure for testing treatment effect heterogeneity.

Can We Predict Skeletal Lesion on Bone Scan Based on Quantitative PSMA PET/CT Features?

OBJECTIVE: The increasing use of PSMA-PET/CT for restaging prostate cancer (PCa) leads to a patient shift from a non-metastatic situation based on conventional imaging (CI) to a metastatic situation. Since established therapeutic pathways have been designed according to CI, it is unclear how this should be translated to the PSMA-PET/CT results. This study aimed to investigate whether PSMA-PET/CT and clinical parameters could predict the visibility of PSMA-positive lesions on a bone scan (BS). METHODS: In four different centers, all PCa patients with BS and PSMA-PET/CT within 6 months without any change in therapy or significant disease progression were retrospectively selected. Up to 10 non-confluent clear bone metastases were selected per PSMA-PET/CT and SUVmax, SUVmean, PSMAtot, PSMAvol, density, diameter on CT, and presence of cortical erosion were collected. Clinical variables (age, PSA, Gleason Score) were also considered. Two experienced double-board physicians decided whether a bone metastasis was visible on the BS, with a consensus readout for discordant findings. For predictive performance, a random forest was fit on all available predictors, and its accuracy was assessed using 10-fold cross-validation performed 10 times. RESULTS: A total of 43 patients were identified with 222 bone lesions on PSMA-PET/CT. A total of 129 (58.1%) lesions were visible on the BS. In the univariate analysis, all PSMA-PET/CT parameters were significantly associated with the visibility on the BS (p < 0.001). The random forest reached a mean accuracy of 77.6% in a 10-fold cross-validation. CONCLUSIONS: These preliminary results indicate that there might be a way to predict the BS results based on PSMA-PET/CT, potentially improving the comparability between both examinations and supporting decisions for therapy selection.

Determinants of international football performance: Empirical evidence from the 1994-2022 FIFA World Cup.

In the past 30 years, the global influence of the FIFA World Cup has continued to grow. According to statistics, the final match of the 2022 World Cup in Qatar attracted an audience of over 3 billion. Nowadays, nations (and regions) emphasize the correlation between overall social progress, football tradition, and national sporting prowess. This study aims to comprehend the dynamics of international football development and secure a competitive edge in the global arena. The study collected panel data from 68 countries (regions) participating in the final stage of the World Cup from 1994 to 2022. It modelled them using a generalized linear mixed model (GLMM) to reveal the macro determinants of success and winning patterns in international football performance. The findings show that (1) football tradition's "characteristic values" (e.g. World Cup host, the experience of hosting the World Cup and the number of all-time World Cup winners) and national comprehensive sporting strength (as represented by the Olympic Games results) have significantly contributed to the national team's World Cup performance; (2) the country's Human Development Index (HDI) has a significant negative impact on World Cup performance; (3) the history of football participation (e.g. the number of years of membership in the Intercontinental Football Association (IFA), history of professional leagues) will have little impact on World Cup performance; (4) two interactive variables: population scale × national comprehensive sporting strength (GPOP × CSOGMedals) and economic level × football tradition (lnGDP × PLHistory), have a negative and positive combined effect on World Cup performance, respectively.

Bayesian model selection for generalized linear mixed models.

We propose a Bayesian model selection approach for generalized linear mixed models (GLMMs). We consider covariance structures for the random effects that are widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. Since the random effects cannot be integrated out of GLMMs analytically, we approximate the integrated likelihood function using a pseudo-likelihood approach. Our Bayesian approach assumes a flat prior for the fixed effects and includes both approximate reference prior and half-Cauchy prior choices for the variances of random effects. Since the flat prior on the fixed effects is improper, we develop a fractional Bayes factor approach to obtain posterior probabilities of the several competing models. Simulation studies with Poisson GLMMs with spatial random effects and overdispersion random effects show that our approach performs favorably when compared to widely used competing Bayesian methods including deviance information criterion and Watanabe-Akaike information criterion. We illustrate the usefulness and flexibility of our approach with three case studies including a Poisson longitudinal model, a Poisson spatial model, and a logistic mixed model. Our proposed approach is implemented in the R package GLMMselect that is available on CRAN.

The effect of productive vocabulary knowledge on second language comprehension.

Second language learners tend to focus more on learning the meaning of vocabulary than on how to use it in their speech and writing. Although comprehensive vocabulary knowledge is necessary for understanding sentences, productive vocabulary knowledge may also have a positive impact on sentence comprehension. Most studies examining the relationship between production and comprehension have compared these abilities between participants or evaluated unrelated criteria between tasks, which may be insufficient for examining the direct effects of productive knowledge on sentence comprehension. Our study investigates changes in sentence comprehension speed during listening, and we used a within-subjects comparison to examine the effect of productive vocabulary knowledge or the lack thereof. We applied generalized linear mixed models to investigate productive vocabulary knowledge effects by partialing out other influential factors, such as confidence, frequency of target words, stimulus duration, and individual differences. The results showed that the sentences with a producible phrase were processed faster than the sentences that included phrases with only comprehensive knowledge or no comprehension. The effect of productive vocabulary knowledge on the speed of sentence comprehension was directly examined with a within-subject comparison, and its contribution was clearly found. This study emphasizes the value of productive vocabulary knowledge acquisition in enhancing the speed of sentence comprehension.

Impact of urbanization on morbidity of hepatitis A: a national panel study in China during 2005-2018.

BACKGROUND: The effect of urbanization on the morbidity of hepatitis A remains unclear. We aimed to estimate the association between various urbanization-related indices and hepatitis A morbidity in China. METHODS: Data on the annual morbidity of hepatitis A, urbanization-related measures (i.e., gross domestic product per capita, the number of hospitalization beds per 1000 persons, illiteracy rate, tap water coverage, motor vehicles per 100 persons, population density, and the proportion of arable land), and meteorological factors in 31 provincial-level administrative divisions of Chinese mainland during 2005-2018 were collected from the National Population and Health Science Data Sharing Platform, China Statistical Yearbooks, and the China Meteorological Data Sharing Service System, respectively. Generalized linear mixed models were applied to quantify the impacts of different urbanization-related indices on the morbidity of hepatitis A in China after adjusting for covariates. RESULTS: A total of 537,466 hepatitis A cases were reported in China during 2005-2018. The annual morbidity had a decline of 79.4% from 5.64 cases to 1.16 cases per 100,000 people. There were obvious spatial variations with higher morbidity in western China. Nationally, gross domestic product per capita and the number of hospitalization beds per 1000 persons increased from 14,040 to 64,644 CNY and from 2.45 to 6.03 during 2005-2018, respectively. The illiteracy rate decreased from 11.0 to 4.9%. Gross domestic product per capita [relative risk (RR) = 0.96, 95% confidence interval (CI): 0.92-0.99], and the number of hospitalization beds per 1000 persons (RR = 0.79, 95% CI: 0.75-0.83) were associated with the declined morbidity of hepatitis A. By contrast, the increased morbidity of hepatitis A was linked to the illiteracy rate (RR = 1.04, 95% CI: 1.02-1.06). Similar influential factors were detected for children and adults, with greater effects witnessed for children. CONCLUSIONS: People in the western region suffered the heaviest burden of hepatitis A in Chinese mainland. Nationally, there was a sharp decline in the morbidity of hepatitis A. The urbanization process was associated with the reduction of hepatitis A morbidity in China during 2005-2018.

Inference of differentially expressed genes using generalized linear mixed models in a pairwise fashion.

Background: Technological advances involving RNA-Seq and Bioinformatics allow quantifying the transcriptional levels of genes in cells, tissues, and cell lines, permitting the identification of Differentially Expressed Genes (DEGs). DESeq2 and edgeR are well-established computational tools used for this purpose and they are based upon generalized linear models (GLMs) that consider only fixed effects in modeling. However, the inclusion of random effects reduces the risk of missing potential DEGs that may be essential in the context of the biological phenomenon under investigation. The generalized linear mixed models (GLMM) can be used to include both effects. Methods: We present DEGRE (Differentially Expressed Genes with Random Effects), a user-friendly tool capable of inferring DEGs where fixed and random effects on individuals are considered in the experimental design of RNA-Seq research. DEGRE preprocesses the raw matrices before fitting GLMMs on the genes and the derived regression coefficients are analyzed using the Wald statistical test. DEGRE offers the Benjamini-Hochberg or Bonferroni techniques for P-value adjustment. Results: The datasets used for DEGRE assessment were simulated with known identification of DEGs. These have fixed effects, and the random effects were estimated and inserted to measure the impact of experimental designs with high biological variability. For DEGs' inference, preprocessing effectively prepares the data and retains overdispersed genes. The biological coefficient of variation is inferred from the counting matrices to assess variability before and after the preprocessing. The DEGRE is computationally validated through its performance by the simulation of counting matrices, which have biological variability related to fixed and random effects. DEGRE also provides improved assessment measures for detecting DEGs in cases with higher biological variability. We show that the preprocessing established here effectively removes technical variation from those matrices. This tool also detects new potential candidate DEGs in the transcriptome data of patients with bipolar disorder, presenting a promising tool to detect more relevant genes. Conclusions: DEGRE provides data preprocessing and applies GLMMs for DEGs' inference. The preprocessing allows efficient remotion of genes that could impact the inference. Also, the computational and biological validation of DEGRE has shown to be promising in identifying possible DEGs in experiments derived from complex experimental designs. This tool may help handle random effects on individuals in the inference of DEGs and presents a potential for discovering new interesting DEGs for further biological investigation.

The nature and persistence of the effects of posthypnotic suggestions on food preferences: The final report of an online study.

The persistence of food preferences, which are crucial for diet-related decisions, is a significant obstacle to changing unhealthy eating behavior. To overcome this obstacle, the current study investigates whether posthypnotic suggestions (PHSs) can enhance food-related decisions by measuring food choices and subjective ratings. After assessing hypnotic susceptibility in Session 1, at the beginning of Session 2, a PHS was delivered aiming to increase the desirability of healthy food items (e.g., vegetables and fruit). After the termination of hypnosis, a set of two tasks was administrated twice, once when the PHS was activated and once deactivated in counterbalanced order. The task set consisted of rating 170 pictures of food items, followed by an online supermarket where participants were instructed to select enough food from the same item pool for a fictitious week of quarantine. After 1 week, Session 3 mimicked Session 2 without renewed hypnosis induction to assess the persistence of the PHS effects. The Bayesian hierarchical modeling results indicate that the PHS increased preferences and choices of healthy food items without altering the influence of preferences in choices. In contrast, for unhealthy food items, not only both preferences and choices were decreased due to the PHS, but also their relationship was modified. That is, although choices became negatively biased against unhealthy items, preferences played a more dominant role in unhealthy choices when the PHS was activated. Importantly, all effects persisted over 1 week, qualitatively and quantitatively. Our results indicate that although the PHS affected healthy choices through resolve, i.e., preferred more and chosen more, unhealthy items were probably chosen less impulsively through effortful suppression. Together, besides the translational importance of the current results for helping the obesity epidemic in modern societies, our results contribute theoretically to the understanding of hypnosis and food choices.

Effect of Average Relative Humidity on Epistaxis.

Background Epistaxis is a very common symptom. The occurrence of epistaxis may be affected by dry environments, but there are some differences among previous reports and this view is controversial. Objective We investigated the relationship between the number of epistaxes and daily average relative humidity. Methods Data on patients with epistaxis between March 2011 and February 2021 were collected from two hospitals. The daily average relative humidity was examined, and the change in the number of patients with epistaxis due to humidity was investigated using a generalized linear mixed model. Results A total of 4184 cases of epistaxis were identified. The number of epistaxis cases per day was significantly associated with the daily average relative humidity (p < 0.001). One percent increment in average relative humidity decreases the number of epistaxis cases per day by 1.1%. Conclusion A negative correlation was found to exist between daily average relative humidity and occurrences of epistaxis.

Simulating and estimating agreement in the presence of multiple raters and covariates.

Cohen's and Fleiss's kappa are popular estimators for assessing agreement among two and multiple raters, respectively, for a binary response. While additional methods have been developed to account for multiple raters and covariates, they are not always applicable, rarely used, and none simplify to Cohen's kappa. Furthermore, there are no methods to simulate Bernoulli observations under the kappa agreement structure such that the developed methods could be adequately assessed. This manuscript overcomes these shortfalls. First, we developed a model-based estimator for kappa that accommodates multiple raters and covariates through a generalized linear mixed model and encompasses Cohen's kappa as a special case. Second, we created a framework to simulate dependent Bernoulli observations that upholds all 2-tuple pair of rater's kappa agreement structure and includes covariates. We used this framework to assess our method when kappa was nonzero. Simulations showed that Cohen's and Fleiss's kappa estimates were inflated unlike our model-based kappa. We analyzed an Alzheimer's disease neuroimaging study and the classic cervical cancer pathology study. The proposed model-based kappa and advancement in simulation methodology demonstrates that the popular approaches of Cohen's and Fleiss's kappa are poised to yield invalid conclusions while our work overcomes shortfalls, leading to improved inferences.

Predictors of Viral Load Status Over Time Among HIV Infected Adults Under HAART in Zewditu Memorial Hospital, Ethiopia: A Retrospective Study.

Background: HIV attacks the CD4 cells which are responsible for the body's immune response to infectious agents. The main objective of this study was to identify predictors of viral load status over time among HIV patients under HAART in Zewditu Memorial Hospital. Methods: A retrospective institutional-based cohort study design was conducted on 161 HIV-infected adults under HAART whose follow-ups were from January 2014 up to December 2017. A generalized linear mixed-effects model was conducted to infer predictors of the status of viral load at 95% of CI). Results: The descriptive statistics revealed that about 55.9% of the adults under treatment had a detected viral load status. Among the potential predictors, visiting time of patients (AOR = 0.731, 95%: (0.634,0.842) and p-value <0.01), age of patients (AOR = 1.0666, 95% CI: (1.0527,1.0917) and p-value <0.01), weight (AOR=. 0.904, 95% CI: (0.862, 0.946) and p-value <0.01), baseline CD4 cell count (AOR = 0.996, 95% CI: (0.994, 0.998) and P-value <0.01), educated patients (AOR = 0.030, 95% CI: (0.002, 0.385) and p-value=0.0053), rural patients (AOR = 6.30,95% CL: (1.78, 2.25) and p-value=0.0043), working status patients (AOR = 0.5905, 95% CI: (0.547,0.638), p-value <0.01), poor adherent patients (AOR = 1.120, 95% CI; (1.035,1.391) and p-value = 0.016) and patients disclosed the disease status (AOR = 0.195, 95% CI: (0.023, 0.818) and p-value=0.0134) significantly affected the detection status of viral loads, keeping all other covariates constant. Conclusion: The predictor variables; visiting times, the weight of patients, residence area, age of patients, educational level, clinical stages, functional status, baseline CD4 cell count, adherence status, and disclosure status of the disease statistically and significantly affected the status of viral load. Hence, health-related education should be given for patients to disclose their disease status, to be good adherents based on the prescription given to the health staff. Due attentions should be given for rural and uneducated patients. Attention should be forwarded to for non-adherent patients to follow the instruction given by the health staff.